A Swedish engineer has demonstrated a bizarre 76-rotor'flying carpet' that can lift an adult several feet off the ground to soar through the air. The manned drone, dubbed'chAIR,' uses dozens of electric motors and cost roughly $10,000 to make, according to creator Axel Borg. Following the first successful flight of the craft last month, Borg has revealed footage from a'playtime' test run through the forest, where it navigates noisily through the trees and high grass carrying its solo pilot. A Swedish engineer has demonstrated a bizarre 76-rotor'flying carpet' that can lift an adult several feet off the ground to soar through the air. The manned drone, dubbed'chAIR,' uses dozens of electric motors and cost roughly $10,000 to make The chAIR VTOL drone uses 76 Elite 5010 274Kv motors to power its huge array of rotors, which are segmented into four circular multi-rotor components.
A newly discovered alien world has three masters. The planet -- which is about four times more massive than Jupiter and located about 340 light-years from Earth -- orbits the brightest star in a three-star system. The two other stars in the system also orbit the brightest star, circling one another like a dumbbell and exerting their gravitational influence over the planet, named HD 131399Ab. "For about half of the planet's orbit, which lasts 550 Earth-years, three stars are visible in the sky," Kevin Wagner, the lead author of a study in the journal Science detailing the new finding, said in a statement. For much of the planet's year the stars appear close together, giving it a familiar night-side and day-side with a unique triple-sunset and sunrise each day.
Multi-agent Pathfinding is a relevant problem in a wide range of domains, for example in robotics and video games research. Formally, the problem considers a graph consisting of vertices and edges, and a set of agents occupying vertices. An agent can only move to an unoccupied, neighbouring vertex, and the problem of finding the minimal sequence of moves to transfer each agent from its start location to its destination is an NP-hard problem. We present Push and Rotate, a new algorithm that is complete for Multi-agent Pathfinding problems in which there are at least two empty vertices. Push and Rotate first divides the graph into subgraphs within which it is possible for agents to reach any position of the subgraph, and then uses the simple push, swap, and rotate operations to find a solution; a post-processing algorithm is also presented that eliminates redundant moves. Push and Rotate can be seen as extending Luna and Bekris's Push and Swap algorithm, which we showed to be incomplete in a previous publication. In our experiments we compare our approach with the Push and Swap, MAPP, and Bibox algorithms. The latter algorithm is restricted to a smaller class of instances as it requires biconnected graphs, but can nevertheless be considered state of the art due to its strong performance. Our experiments show that Push and Swap suffers from incompleteness, MAPP is generally not competitive with Push and Rotate, and Bibox is better than Push and Rotate on randomly generated biconnected instances, while Push and Rotate performs better on grids.
Much of the literature on suboptimal, polynomial-time algorithms for multi-agent path finding focuses on undirected graphs, where motion is permitted in both directions along a graph edge. Despite this, traveling on directed graphs is relevant in navigation domains, such as path finding in games, and asymmetric communication networks. We consider multi-agent path finding on strongly biconnected directed graphs. We show that all instances with at least two unoccupied positions have a solution, except for a particular, degenerate subclass where the graph has a cyclic shape. We present diBOX, an algorithm for multi-agent path finding on strongly biconnected directed graphs.
Connected and automated vehicles (CAVs) have attracted more and more attention recently. The fast actuation time allows them having the potential to promote the efficiency and safety of the whole transportation system. Due to technical challenges, there will be a proportion of vehicles that can be equipped with automation while other vehicles are without automation. Instead of learning a reliable behavior for ego automated vehicle, we focus on how to improve the outcomes of the total transportation system by allowing each automated vehicle to learn cooperation with each other and regulate human-driven traffic flow. One of state of the art method is using reinforcement learning to learn intelligent decision making policy. However, direct reinforcement learning framework cannot improve the performance of the whole system. In this article, we demonstrate that considering the problem in multi-agent setting with shared policy can help achieve better system performance than non-shared policy in single-agent setting. Furthermore, we find that utilization of attention mechanism on interaction features can capture the interplay between each agent in order to boost cooperation. To the best of our knowledge, while previous automated driving studies mainly focus on enhancing individual's driving performance, this work serves as a starting point for research on system-level multi-agent cooperation performance using graph information sharing. We conduct extensive experiments in car-following and unsignalized intersection settings. The results demonstrate that CAVs controlled by our method can achieve the best performance against several state of the art baselines.